Evaluating Job Training in Two Chinese Cities

Recent years have seen a surge in the evidence on the impacts of active labor market programs for numerous countries. However, little evidence has been presented on the effectiveness of such programs in China. Recent economic reforms, associated massive lay-offs, and accompanying public retraining programs make China fertile ground for rigorous impact evaluations. This study evaluates retraining programs for laid-off workers in the cities of Shenyang and Wuhan using a comparison group design. To our knowledge, this is the first evaluation of its kind in China. The evidence suggests that retraining helped workers find jobs in Wuhan, but had little effect in Shenyang. However, in terms of earnings impacts, retraining appears to have increased earnings in Shenyang but not in Wuhan. The study raises questions about the overall effectiveness of retraining expenditures, and it offers some directions for policymakers about future interventions to help laid-off workers.Active labor market programs, job training, impact evaluation, propensity score matching, China

A framework for space-efficient string kernels

String kernels are typically used to compare genome-scale sequences whose length makes alignment impractical, yet their computation is based on data structures that are either space-inefficient, or incur large slowdowns. We show that a number of exact string kernels, like the $k$-mer kernel, the substrings kernels, a number of length-weighted kernels, the minimal absent words kernel, and kernels with Markovian corrections, can all be computed in $O(nd)$ time and in $o(n)$ bits of space in addition to the input, using just a $\mathtt{rangeDistinct}$ data structure on the Burrows-Wheeler transform of the input strings, which takes $O(d)$ time per element in its output. The same bounds hold for a number of measures of compositional complexity based on multiple value of $k$, like the $k$-mer profile and the $k$-th order empirical entropy, and for calibrating the value of $k$ using the data

On the NP-Hardness of Approximating Ordering Constraint Satisfaction Problems

We show improved NP-hardness of approximating Ordering Constraint Satisfaction Problems (OCSPs). For the two most well-studied OCSPs, Maximum Acyclic Subgraph and Maximum Betweenness, we prove inapproximability of $14/15+\epsilon$ and $1/2+\epsilon$. An OCSP is said to be approximation resistant if it is hard to approximate better than taking a uniformly random ordering. We prove that the Maximum Non-Betweenness Problem is approximation resistant and that there are width-$m$ approximation-resistant OCSPs accepting only a fraction $1 / (m/2)!$ of assignments. These results provide the first examples of approximation-resistant OCSPs subject only to P $\neq$ \NP

Impossibility of independence amplification in Kolmogorov complexity theory

The paper studies randomness extraction from sources with bounded independence and the issue of independence amplification of sources, using the framework of Kolmogorov complexity. The dependency of strings $x$ and $y$ is ${\rm dep}(x,y) = \max\{C(x) - C(x \mid y), C(y) - C(y\mid x)\}$, where $C(\cdot)$ denotes the Kolmogorov complexity. It is shown that there exists a computable Kolmogorov extractor $f$ such that, for any two $n$-bit strings with complexity $s(n)$ and dependency $\alpha(n)$, it outputs a string of length $s(n)$ with complexity $s(n)- \alpha(n)$ conditioned by any one of the input strings. It is proven that the above are the optimal parameters a Kolmogorov extractor can achieve. It is shown that independence amplification cannot be effectively realized. Specifically, if (after excluding a trivial case) there exist computable functions $f_1$ and $f_2$ such that ${\rm dep}(f_1(x,y), f_2(x,y)) \leq \beta(n)$ for all $n$-bit strings $x$ and $y$ with ${\rm dep}(x,y) \leq \alpha(n)$, then $\beta(n) \geq \alpha(n) - O(\log n)$

A High Quartets Distance Construction

Given two binary trees on N labeled leaves, the quartet distance between the trees is the number of disagreeing quartets. By permuting the leaves at random, the expected quartet distance between the two trees is 23(N4) . However, no strongly explicit construction reaching this bound asymptotically was known. We consider complete, balanced binary trees on N=2n leaves, labeled by n bits long sequences. Ordering the leaves in one tree by the prefix order, and in the other tree by the suffix order, we show that the resulting quartet distance is (23+o(1))(N4) , and it always exceeds the 23(N4) bound

Practical private database queries based on a quantum key distribution protocol

Private queries allow a user Alice to learn an element of a database held by a provider Bob without revealing which element she was interested in, while limiting her information about the other elements. We propose to implement private queries based on a quantum key distribution protocol, with changes only in the classical post-processing of the key. This approach makes our scheme both easy to implement and loss-tolerant. While unconditionally secure private queries are known to be impossible, we argue that an interesting degree of security can be achieved, relying on fundamental physical principles instead of unverifiable security assumptions in order to protect both user and database. We think that there is scope for such practical private queries to become another remarkable application of quantum information in the footsteps of quantum key distribution.Comment: 7 pages, 2 figures, new and improved version, clarified claims, expanded security discussio

A Formal Study of the Privacy Concerns in Biometric-Based Remote Authentication Schemes

With their increasing popularity in cryptosystems, biometrics have attracted more and more attention from the information security community. However, how to handle the relevant privacy concerns remains to be troublesome. In this paper, we propose a novel security model to formalize the privacy concerns in biometric-based remote authentication schemes. Our security model covers a number of practical privacy concerns such as identity privacy and transaction anonymity, which have not been formally considered in the literature. In addition, we propose a general biometric-based remote authentication scheme and prove its security in our security model

Order-Revealing Encryption and the Hardness of Private Learning

An order-revealing encryption scheme gives a public procedure by which two ciphertexts can be compared to reveal the ordering of their underlying plaintexts. We show how to use order-revealing encryption to separate computationally efficient PAC learning from efficient $(\epsilon, \delta)$-differentially private PAC learning. That is, we construct a concept class that is efficiently PAC learnable, but for which every efficient learner fails to be differentially private. This answers a question of Kasiviswanathan et al. (FOCS '08, SIAM J. Comput. '11). To prove our result, we give a generic transformation from an order-revealing encryption scheme into one with strongly correct comparison, which enables the consistent comparison of ciphertexts that are not obtained as the valid encryption of any message. We believe this construction may be of independent interest.Comment: 28 page